Seo et al. BMC Medical Genetics(2020) 21:241
https://doi.org/10.1186/s12881-020-01177-w

RESEARCH ARTICLE

Open AccessA non-synonymous variant rs12614 ofcomplement factor B associated with riskof chronic hepatitis B in a Korean
population
Jung Yeon Seo1,2†, Joong-Gon Shin1,3†, Byeong Ju Youn2, Suhg Namgoong2,4, Hyun Sub Cheong4,
Lyoung Hyo Kim4, Ji On Kim3, Hyoung Doo Shin2,3,4* and Yoon Jun Kim5*

Abstract
Background: Hepatitis B is known to cause several forms of liver diseases including chronic hepatitis B (CHB), andhepatocellular carcinoma. Previous genome-wide association study of CHB risk has demonstrated that rs12614 ofcomplement factor B (CFB) was significantly associated with CHB risk. In this study, fine-mapping study of previously
reported GWAS single nucleotide polymorphism (SNP; CFB rs12614) was performed to validate genetic effect of
rs12614 on CHB susceptibility and identify possible additional causal variants around rs12614 in a Korean population.
This association study was conducted in order to identify genetic effects of CFB single nucleotide polymorphisms(SNPs) and to identify additional independent CHB susceptible causal markers within a Korean population.
Methods: A total of 10 CFB genetic polymorphisms were selected and genotyped in 1716 study subjects
comprised of 955 CHB patients and 761 population controls.
Results: A non-synonymous variant, rs12614 (Arg32Trp) in exon2 of CFB, had significant associations with risk of
CHB (odds ratio = 0.43, P = 5.91 × 10− 10). Additional linkage disequilibrium and conditional analysis confirmed thatrs12614 had independent genetic effect on CHB susceptibility with previously identified CHB markers. The genetic
risk scores (GRSs) were calculated and the CHB patients had higher GRSs than the population controls. Moreover,OR was found to increase significantly with cumulative GRS.
Conclusions: rs12614 showed significant genetic effect on CHB risk within the Korean population. As such rs12614
may be used as a possible causal genetic variant for CHB susceptibility.
Keywords: CFB, Genetic risk scores, Hepatitis B, Korean population, Liver disease* Correspondence: hdshin@sogang.ac.kr; yoonjun@snu.ac.kr†
Jung Yeon Seo and Joong Gon Shin contributed equally to this work.
2
Department of Life Science, Sogang University, Seoul 04107, Republic of
Korea
5
Department of Internal Medicine and Liver Research Institute, Seoul
National University, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of
Korea
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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Seo et al. BMC Medical Genetics(2020) 21:241

BackgroundHepatitis B, caused by the hepatitis B virus (HBV) infection, is known to cause several liver diseases including
chronic hepatitis B (CHB), cirrhosis, and hepatocellular
carcinoma (HCC) [1, 2]. According to the 2015 WHO
report, HBV infection affects 3.5% of the world population (257 million individuals) and is especially prevalentin Asian populations [3]. Several genome-wide association studies (GWASs) on CHB risk have been conducted on Asian populations and have found that CHB
risk associated loci are typically located in humanleukocyte antigen (HLA) regions, such as HLA-DP and
HLA-DQ [4–7], in Chinese, Japanese, and Korean populations. According to our previous GWASs, several
genes in HLA regions, such as transcription factor 19(TCF19), and valyl-tRNA synthetase 2 (VARS2)-surfactant associated 2 (SFTA2), euchromatin histone-lysinemethyltransferase 2 (EHMT2), showed significant genetic
effects on CHB susceptibility in a Korean population [5–9]. Interestingly, genetic variants of CFB, found on the
nearby gene of EHMT2, have strong association with
CHB susceptibility in Chinese studies [10–12].
CFB located on HLA genomic region is essential for
regulating T-cell mediated innate immunity in the complement system [13–15]. A number of studies have demonstrated that CFB genetic variants are also associatedwith several diseases related to innate immune responses, anterior uveitis and Vogt-Koyanagi-Harada disease [16, 17]. This study conducted association analysis
between CFB SNPs and CHB susceptibility to validate
genetic effect of rs12614 and identify possible additionalcausal variants around rs12614 in a Korean population
by fine-mapping of CFB region. Furthermore, the genetic
risk scores (GRSs) of all known CHB risk makers were
calculated to investigate the cumulative genetic effects of
CHB susceptibility in individuals.
Methods
Study subjects

In this study, a total of 1716 subjects which were consistof 955 cases and 761 controls were recruited and investigated for identifying genetic effects on CHB. The 955
patients with CHB were obtained from the outpatient
clinic of the Liver Unit and the Center for Health
Promotion at Seoul National University Hospital, Ajou
University Medical Center (Suwon, Korea), and Ulsan
University Hospital (Ulsan, Korea). Among the CHB patients, 296 patients were also diagnosed with HCC. The
761 population controls (PCs) were provided by Korea
BioBank, the Center for Genome Science, Korea Centers
for Disease Control and Prevention, and the National Institute of Health. Seropositivity of the hepatitis B surface(HBsAg; Enzygnost® HBsAg 5.0; Dade Behring, Marburg,
Germany) over a 6-month period was used for inclusion

Page 2 of 9

criterion for diagnosing patients with chronic HBV
infection (Supplementary Table 1). The detailed experimental procedures for HBsAg detection using Enzygnost® HBsAg 5.0 Kit assay is described in elsewhere [18].
Diagnosis of HCC was based on imaging findings ofnodules that were larger than 1 cm, showing intense arterial uptake, followed by washout of contrast in thevenous-delayed phases, in a 4-phase multi-detector CT
scan or dynamic contrast enhanced MRI and/or biopsy[19]. Though individuals with HBsAg (−) and anti-HBc(+) (spontaneously cleared for viral infection) are thebest disease controls, individuals with an unknown response to HBV infection were used as the population
controls, and some of them still have a chance to CHB
and/or HCC when exposed to HBV. The study protocol
complied with the Declaration of Helsinki. The study
was approved by the institutional review board of Seoul
National University Hospital, Ajou University Medical
Center, and Ulsan University Hospital. All the subjects
participating in the study provided written informed
consent.
SNP genotyping

Following criteria were adopted for SNP selection: 1)
Candidate SNPs of the genomic region around CFB(CFB gene with 2 kb upstream (to include promoter region) and 500 bp downstream regions; Chr6: 31,911,72131,920,361) were extracted from 1000 genomes Japaneseand Han Chinese data, and minor allele frequency(MAF) and linkage disequilibrium (LD) status of theextracted SNPs were calculated. 2) Using NCBI dbSNP,
investigate the functional location of the SNPs (upstream
variant (2 kb), 5-prime UTR variant, missense,
synonymous-codon, intron variant, downstream variant(500 bp), 3-prime UTR variant) extracted in 1). 3) Basedon the 1) and 2), among the SNPs with high LD (r2 >0.98), SNP with relatively frequent (MAF > 5%) andfunctional effect based on the position was selected. And
promoter region and non-synonymous SNPs with low
frequency (MAF ≤ 5%) are additionally selected. As results, 5 tagging SNPs (rs1048709, rs537160, rs541862,
rs4151657 and rs2072633) were selected along with 5
non-synonymous SNPs (rs4151667, rs12614, rs641153,
rs117314762 and rs45484591). A total of 10 SNPs were
genotyped in 955 CHB patients and 761 healthy controls. Genotyping reactions were performed by using
BioMark HD system (Fluidigm 192.24 SNPtype™, San
Francisco, CA, USA). The primer pools were designed
for Specific target amplifications, Allele-specific and
Locus-specific primers to detect candidate SNPs, and all
the primers for 10 investigated SNPs in this study were
designed and provided by the manufacturer of Fluidigm
system (Fluidigm Corp., San Francisco, CA, USA). The
additional workflow was followed by the manufacturer’sSeo et al. BMC Medical Genetics(2020) 21:241

instructions for using the Integrated IFC Controller RX,
FC1 Cycler, and EP1 Reader. Signal intensities for genotyping calling were scanned using the EP1 data collection and SNP Genotyping analysis software (Fluidigm
Corp., San Francisco, CA, USA). The locations of the investigated SNPs are shown in Supplementary Figure 1A.
Statistical analysis

LD status of the investigated SNPs were calculated withexamination of Lewontin’s D’ (|D’|) and the LD coefficientr2 between all pairs of bi-allelic loci using Haploview
v4.2(http://www.broadinstitute.org/mpg/haploview)[20]. Comparison of genotype distributions, including
MAF and Hardy-Weinberg Equilibrium (HWE), between CHB patients and controls and and calculating
odds ratios (ORs), 95% confidence intervals, and corresponding P-values was carried out with a logistic regression model adjusted for age (continuous value) andsex (male = 0, female = 1) as covariates using SAS, version 9.4 (SAS Inc., Cary, NC, USA). In corrections for
multiple comparisons, Bonferroni correction for multiple testing was applied. Conditional logistic regression
analysis was performed to investigate whether the novel
significant association signal of investigated SNP was
independent or affected by previously known CHB
markers. Allele test based on the allele distribution ofeach SNP was also performed to assess the detailed
genetic effects. Ten previously reported CHB susceptible loci in a Korean population (rs9277535 of HLADPB1; rs3077 of HLA-DPA1; rs2856718 of HLA-DQB1;
rs7453920 of HLA-DQB2; rs1419881 of TCF19;
rs1265163 of OCT4; rs652888 and rs35875104 of
EHMT2; rs9394021 and rs2517459 of VARS2-SFTA)[5–9] were used for the conditional analysis and allele
test. Based on the results from allele test, GRSs were
calculated. The detailed calculation method for GRSs
was described in elsewhere [18].

Results

Page 3 of 9analysis under an additive model was conducted. Analysis results indicated that rs12614 was significantly associated with risk of CHB even after applying Bonferroni
correction for multiple testing (OR = 0.43, P = 5.91 ×
10− 10, Pcorr = 2.36 × 10− 8; Table 1). In order to validate
the genetic effects of rs12614, association analysis was
conducted using the training and test sets from the subjects in this study (Supplementary Table 3). Additional
subgroup analysis was performed to investigate theassociation between CFB SNPs and CHB-related HCC
progression. Again, analysis results found that, rs12614
had significant associations with risk of CHB in both the
HCC (−) CHB and the HCC (+) CHB subgroups (P =6.60 × 10− 8 and 3.10 × 10− 6, respectively) even after
Bonferroni correction was applied for multiple testing(Pcorr = 2.64 × 10− 6 and 1.24 × 10− 4, respectively). However, rs12614 did not show significant genetic effect on
CHB-related HCC progression (P > 0.05).
Independent genetic effect of rs12614 on CHB risk

In order to understand the association between rs12614
and CHB risk, particularly with respect to independent
genetic effect on CHB susceptibility, this study conducted LD calculations and conditional analysis on 10
previously identified CHB susceptibility markers(rs9277535 of HLA-DPB1; rs3077 of HLA-DPA1;
rs2856718 of HLA-DQB1; rs7453920 of HLA-DQB2;
rs1419881 of TCF19; rs1265163 of OCT4; rs652888 andrs35875104 of EHMT2; rs9394021 and rs2517459 of
VARS2-SFTA). Supplementary Figure 2 shows the LD
plot of rs12614 and the 10 CHB susceptibility markers.
The results show that CFB rs12614 did not display tightLDs with any known, nearby CHB-susceptible loci (pairwise r2 ≤ 0.15; Supplementary Figure 2). In addition,
when adjusting for previously identified CHB markers,
rs12614 maintained significant association with CHB risk
(P < 0.05; Table 2), indicating that rs12614 had independent genetic effect on CHB susceptibility to previously identified CHB risk markers.

Genotyping of CFB genetic variants

A total of 10 CFB SNPs were selected and genotyped in
1716 Korean subjects, comprised of 955 CHB patientsand 761 population controls (Supplementary Table 1).
Patients were divided into two subgroups, 659 HCC (−)
CHB cases and 296 HCC (+) CHB cases. A gene map
and LD among investigated SNPs are shown in Supplementary Figure 1A and B. Detailed information on theinvestigated SNPs, such as chromosome, position, allele,genotype distribution, heterozygosity, and HWE P, arepresented in Supplementary Table 2.
Association of CFB genetic polymorphisms with CHB risk

In order to investigate the association between CFB genetic polymorphisms and risk of CHB, logistic regression

Cumulative genetic effects of CHB susceptible loci

In order to examine the detailed genetic effects of all 11
CHB susceptible loci including rs12614 (rs12614 of CFB;
rs9277535 of HLA-DPB1; rs3077 of HLA-DPA1;
rs2856718 of HLA-DQB1; rs7453920 of HLA-DQB2;
rs1419881 of TCF19; rs1265163 of OCT4; rs652888 andrs35875104 of EHMT2; rs9394021 and rs2517459 of
VARS2-SFTA) in a Korean population, an allele test was
conducted for each SNP. The GRSs of the genotypes
were calculated using the ORs from allele test (Table 3).
To elucidate the cumulative genetic effects of all 11
CHB loci in the study subjects, the cumulative GRSs
were evaluated. The cumulative GRSs ranged from 5.24
(most protected group) to 17.38 (most susceptible

Seo et al. BMC Medical Genetics(2020) 21:241

Page 4 of 9

Table 1 Association of CFB genetic polymorphisms with the risk of CHB and HCC
Marker

MAF

Comparing groups

Total CHB
(n =
All
1716)
CHB
(n =
955)

HCC
(−)
(n =
659)

HCC
(+)
(n =
f)

PC
CHB vs. PC
(n =
761)
OR
P*
(95%
CI)

HCC (−) vs. PC

HCC (+) vs. PC

HCC (−) vs.
HCC (+)

Pcorr**

OR
(95%
CI)

P*

Pcorr**

OR
(95%
CI)

P*

Pcorr**

OR
(95%
CI)

P*

1.16
(0.65–
2.06)

0.61

–

1.41
(0.70–
2.80)

0.34

–

0.82
(0.41–
1.63)

0.58

3.10 × 1.24 × 1.13
10− 6
10−4
(0.72–
1.78)

0.58

rs4151667

0.018

0.019 0.018 0.022 0.016 1.23
(0.73–
2.08)

0.42

–

rs126140.078

0.053 0.054 0.048 0.112 0.43
(0.33–
0.57)

5.91 ×10−10

2.36 × 0.45
10−8
(0.33–
0.61)

6.60 × 2.64 × 0.39
10− 8
10−6
(0.25–
0.60)

rs641153

0.082

0.083 0.086 0.076 0.080 1.04
(0.81–
1.33)

0.74

–

1.08
(0.83–
1.41)

0.54

–

0.94
(0.66–
1.34)

0.75

–

1.14
(0.80–
1.62)

0.45

rs117314762 0.015

0.013 0.014 0.010 0.018 0.67
(0.38–
1.17)

0.16

–

0.73
(0.40–
1.34)

0.31

–

0.54
(0.22–
1.32)

0.15

–

1.35
(0.53–
3.45)

0.51

rs1048709

0.279

0.269 0.265 0.277 0.293 0.88
(0.76–
1.02)

0.11

–

0.86
(0.73–
1.02)

0.09

–

0.92
(0.74–
1.14)

0.47

–

0.94
(0.75–
1.16)

0.58

rs537160

0.333

0.327 0.322 0.340 0.341 0.93
(0.81–
1.08)

0.39

–

0.91
(0.77–
1.07)

0.26

–

0.99
(0.81–
1.21)

0.95

–

0.91
(0.74–
1.13)

0.43

rs541862

0.082

0.083 0.086 0.076 0.080 1.04
(0.81–
1.33)

0.74

–

1.08
(0.83–
1.41)

0.54

–

0.94
(0.66–
1.34)

0.75

–

1.14
(0.80–
1.62)

0.45

rs4151657

0.323

0.335 0.341 0.321 0.307 1.13
(0.98–
1.30)

0.09

–

1.16
(0.99–
1.36)

0.06

–

1.06
(0.86–
1.30)

0.55

–

1.09
(0.88–
1.34)

0.40

rs45484591

0.001

0.001 0.002 0.000 0.001 0.79
(0.11–
5.66)

0.82

–

1.15
(0.16–
8.22)

0.89

–

–

–

–

–

–

rs2072633

0.477

0.468 0.467 0.471 0.488 0.92
(0.80–
1.05)

0.25

–

0.91
(0.79–
1.06)

0.26

–

0.93
(0.77–
1.13)

0.50

–

0.98
(0.80–
1.19)

0.85

Significant associations are shown in bold face (P < 0.05)
MAF minor allele frequency, CHB chronic hepatitis B, HCC hepatocellular carcinoma, OR odds ratio, CI confidence interval*
P-value of logistic regression analysis under additive model by adjusting for sex and age as covariates**P-value after Bonferroni correction for multiple testinggroup), and CHB patients showed significantly higher
cumulative GRSs than did the healthy control subjects(Supplementary Table 4 and Fig. 1a). It was found thatas cumulative GRSs increased, ORs significantly increased as well. In particular, individuals with GRSs ofless than 7 showed an OR of 0.17 (log10 OR = − 0.77),
while individuals with GRSs of over 14 showed an OR of
3.42 (log10 OR = 0.53) (Fig. 1b).

Discussion
Hepatitis B, an infectious disease with a high rate of incidence in Asian populations [1], is a major cause of CHB,
liver failure, liver cirrhosis, and HCC development, diseases which often result in death [21, 22]. Although themechanisms underlying the different clinical results of
HBV infection have not been fully understood, previous

studies have linked a diverse range of factors such as
viral strain, gender, age of infection, host immune system, and genetic information of the host, with risk of
CHB [23]. When viral infection occurs, several immunerelated genes are activated, leading to disease outbreak.
According to a GWAS conducted on a Chinese population, a CFB genetic variant had significant association
with risk of CHB [10]. In this study, we aimed to 1) validation of GWAS association signal SNP (CFB rs12614)
on CHB susceptibility in a Korean population, and 2)
identification of possible additional causal variants
around GWAS association signal SNP on CHB susceptibility in a Korean population by fine-mapping of CFB
region.
The complement system is composed of over 30
plasma proteins and is activated by microbes orC>T

1.57 × 10−4

4.73 × 10−8

6.97 × 10− 8

2.07 × 10− 8

5.29 × 10− 7

EHMT2
VARS2-SFTA VARS2-SFTA
rs35875104** rs9394021** rs2517459**
5.87 × 10− 8 7.80 × 10−7

HLA-DQB1
HLA-DQB2
TCF19
OCT4
EHMT2
rs2856718** rs7453920** rs1419881** rs1265163** rs652888**

3.52 × 10−5 4.46 × 10− 6

HLA-DPB1
HLA-DPA1
rs9277535** rs3077**

Conditional P-value by

0.42 (0.31–0.56) 5.91 × 10−10 5.94 × 10−6

P-value*Significant associations are shown in bold face (P < 0.05)
OR odds ratio, CI confidence interval*P-value of logistic regression analysis by adjusting for sex and age as covariates**Previously identified CHB marker

rs12614

Marker Allele OR (95% CI)

Table 2 Results of conditional analysis with the previously identified CHB marker

Seo et al. BMC Medical Genetics(2020) 21:241
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Seo et al. BMC Medical Genetics(2020) 21:241

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Table 3 Determination of Genetic Risk Score based on Allele test of CHB susceptible lociGene/Marker

Genotype distribution

CFB/rs12614

c

HLA-DPB1/rs9277535

HLA-DPA1/rs3077cc

HLA-DQB1/rs2856718

HLA-DQB2/rs7453920c

TCF19/rs1419881

c

EHMT2/rs652888cc

OCT4/rs1265163

EHMT2/rs35875104cc

VARS2-SFTA/rs9394021

VARS2-SFTA/rs2517459c

GRSb

Allele test

CHB (n = 955)

PC (n = 761)

AlleleOR (95% CI)

P-valueCC(829)CC(550)C

1–

CT(91)

CT(142)

TT(3)

TT(7)

a

T

0.44 (0.33–0.57)

6.46 × 10

1
− 10

0.44
0.22

GG(420)

GG(184)

G

1

–

1

GA(428)GA(384)A

0.49 (0.43–0.56)

9.47 × 10−24

0.49

AA(107)

AA(193)

GG(489)GG(221)

G

1

–

1

0.25

GA(382)GA(385)A

0.48 (0.41–0.55)

1.84 × 10−24

0.48

AA(84)

AA(155)CC(226)

CC(263)

C

1

–

1

CT(418)CT(373)T

1.72 (1.50–1.97)

3.10 × 10−15

1.72

0.24

TT(311)

TT(125)

GG(743)

GG(461)

G

1

–

1

3.44

GA(196)GA(275)A

0.49 (0.41–0.60)

1.07 × 10−13

0.49

AA(16)

AA(25)TT(419)TT(270)

T

1

–

1

TC(424)

TC(361)C

0.74 (0.64–0.85)

3.46 × 10−5

0.74

0.25

CC(112)CC(130)TT(610)TT(565)

T

1

–

1

0.37

TC(300)

TC(188)C

1.65 (1.37–1.99)

5.14 × 10−8

1.65

CC(45)CC(8)3.3

GG(401)

GG(392)

G

1

–

1

GC(439)GC(299)C

1.32 (1.14–1.53)

1.36 × 10−4

1.32

CC(114)CC(70)

TT(878)TT(650)

T

1

–

1

2.64

TC(75)

TC(108)C

0.53 (0.39–0.71)

2.42 × 10− 5

0.53

CC(2)

CC(3)

0.27

AA(303)AA(189)

A

1

–

1

AG(471)

AG(368)G

0.73 (0.64–0.84)

1.10 × 10−50.73

GG(177)

GG(203)

GG(809)GG(560)

G

1

–

1

0.37

GA(139)GA(193)A

0.53 (0.43–0.67)

3.71 × 10−8

0.53

AA(6)

AA(8)

0.27

Significant associations are shown in bold face (P < 0.05)
CHB chronic hepatitis B, PC population control, OR odds ratio, CI confidence interval, GRS genetic risk scorea
Major and minor alleles were determined in all study subjectsb
GRS was calculated by multiplying the number of minor alleles by effect size (OR) of the SNPcPreviously identified CHB marker in Korean population

antibodies which attached to microbes or other antigens[24, 25]. This system is an innate immune system thathelps operate rapid responses against pathogenic invasions by opsonizing or recruiting inflammatory cells or
pathogen lysis [26]. The complement activations occur
through three pathways: the classical pathway, the lectin
pathway and the alternative pathway. These pathways

are worked through a cascade of enzymes reaction [25,27]. CFB is essential to activate the complement system,
particularly the alternative pathway that is against microbe invasion which includes viruses [28].
Previous Chinese studies have identified CFB genetic
variants which have genetic effect on CHB risk. The
most significant association was identified at rs12614 of

Seo et al. BMC Medical Genetics(2020) 21:241

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Fig. 1 Combined genetic effects of eleven CHB genetic markers on the risk of CHB. a Comparison of GRS between CHB group and PC group.
b Odds ratios of different GRS range in log10 scale. Median genetic risk score range (9.8–11.2) is used as a reference. CHB, chronic hepatitis B; PC,
population control; GRS, genetic risk score

CFB (P = 1.28 × 10− 34 - 4.0 × 10− 3) [10, 11]. In this study,
rs12614 showed the same direction of genetic effect asfound in previous Chinese studies. In order to validate
the associations, we have conducted the validation analysis of CFB genetic variant, rs12614, using random sampling of the training and test sets from the subjects. As
result, all training sets showed significant results and although not all test sets showed significant results due tosmall sample sizes in test sets, the trends of effects were
the same directions (Supplementary Table 3). Moreover,
CFB rs12614 was significantly associated with risk of
CHB in the HCC (−) CHB and the HCC (+) CHB groups(P = 6.60 × 10− 8 and 3.10 × 10− 6, respectively). However,there was no significant genetic effect on CHB-related
HCC progression. Additionally, the rs12614 C > T T
allele was more frequently observed in the PC groupthan the CHB patients with a significance (OR = 0.43,
Pcorr = 2.36 × 10− 8). Considering that individuals with
the non-synonymous variant (rs12614 T allele) had significantly higher CFB expression than those with thers12614 C allele in the Chinese study, it can be seen thatthe rs12614 may affect immune response by influencing
the complement system when viral infection occurs [10].
The rs12614 which is located on coding region of
CFB, C to T allele change causes the amino acid change,
arginine to tryptophan. To predict the effects of thers12614 amino acid change, we conducted in silico analysis using the PolyPhen-2 program (http://genetics.bwh.
harvard.edu/pph2/index.shtml) [29]. The results demonstrated that this amino acid change is predicted to beprobably damaging that means this substitution might
be damaging with high confidence (Supplementary Figure 3A, [29]). Amino acid alignment from the program,arginine at position 32 is highly conserved among species (Supplementary Figure 3B). Additionally, protein

structure prediction was performed using CFSSP: Chou
& Fasman Secondary Structure Prediction Server (http://
www.biogem.org/tool/chou-fasman/index.php)[30].
Changes in protein secondary structure of rs12614 region, from coil structure to helix structure, by aminoacid change from arginine to tryptophan were predicted.
(Supplementary Figure 4). Protein function is closely related to the structure so that amino acids residue
substitution can modify functional sites or protein interactions. And also disease-causing substitutions are more
likely to occur at positions that is conserved throughout
evolution [31], the rs12614 C to T allele substitution
may affect CFB functions. Because the alternative pathway is important to against pathogen invasion, an aminoacid change in CFB important in the alternative pathway
may affect the immune system to against hepatitis B
virus invasion.
Some individuals are more susceptible to diseases
while others are less susceptible. Identification of thegenetic background is key to understand differences inindividuals’ disease susceptibility, and that can potentially lead to the targeting of preventive measures at
those who are at greatest risk [32]. The results of theconditional analysis conducted on the 10 previously
identified markers indicated that rs12614 can be used asa novel causal variant of CHB susceptibility. To elucidate
its cumulative genetic effects, we used odd ratios ofrs12614 and previously identified 10 CHB markers. Consequently, CHB group showed higher GRSs than the PC
group and the higher genetic risk scores range indicatedhigher odds ratios. These implies CHB patients are more
likely to have higher scores than controls.
There is a sampling limitation in this study. While theideal subjects for the control groups would be the people
who are HBsAg (−) and anti-HBc (+) (spontaneouslySeo et al. BMC Medical Genetics(2020) 21:241

Page 8 of 9

cleared), we used population controls with unknown responses to HBV infection. And some individuals in thecontrol group still have a chance of progression to CHB
when exposed to HBV. Although using the population
controls in a case-control study may reduce statisticalpower, it is useful when it is difficult to obtain a sufficient number of disease controls.

HCC: Hepatocellular carcinoma; GWASs: Genome-wide association studies;
HLA: Human leukocyte antigen; TCF19: Transcription factor 19; VARS2SFTA2: Valyl-tRNA synthetase 2-surfactant associated 2; EHMT2: Euchromatin
histone-lysine-methyltransferase 2; PC: Population control; MAF: Minor allele
frequency; LD: Linkage disequilibrium; HWE: Hardy-Weinberg Equilibrium;
ORs: Odds ratios

Conclusions
A non-synonymous variant, rs12614 (Arg32Trp) of CFB
was found to have significant associations with risk of
CHB in a Korean population. Moreover, genetic effect of
rs12614 on CHB risk was independent of all known CHB
risk loci, and rs12614 can be used as possible causal variant of CHB susceptibility. Therefore, the results from thisstudy may help in understanding and predicting genetic
susceptibility to CHB in a Korean population.

Authors’ contributions
Conceptualization: HDS, YJK; methodology: HDS, YJK; investigation: JYS, JGS;
formal analysis: JYS, JGS, BJY, SN, HSC, LHK, JOK; drafting of the manuscript:
JYS, JGS; reviewing and editing the manuscript: BJY, HDS, YJK; all authors
read and approved the final version of the manuscript.

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12881-020-01177-w.
Additional file 1: Supplementary Figure 1. Gene map and LD plot of
CFB. A. Gene map of CFB (complement factor B) on chromosome 6p21.33(6 kb). Black blocks mean coding exons, and white blocks mean 5′ and 3′
UTRs. B. Linkage disequilibrium (LD) plot of CFB polymorphisms.
Numbers in color boxes indicate |D’| values.
Additional file 2: Supplementary Figure 2. LD plot of rs12614 andpreviously identified 10 CHB markers. LD plot of CFB rs12614 andpreviously identified 10 CHB susceptibility markers. Numbers in black andwhite boxes indicate r2 values. LD structure was constructed by
Haploview software.
Additional file 3: Supplementary Figure 3. In silico analysis of CFB
rs12614. A. To predict amino acid change (Arg32Trp) in CFB rs12614
affects protein function, HumDiv model analysis was conducted using
PolyPhen-2 v2.2.2r398. Arg32Trp mutation is predicted to be probably
damaging with a score of 0.985 (http://genetics.bwh.harvard.edu/pph2/
index.shtml). B. Amino acids sequence alignment about diverse speciesof CFB rs12614 was conducted using PolyPhen-2 v2.2.2r398 according to
UniProtKB/UniRef100 (http://genetics.bwh.harvard.edu/pph2/index.shtml).
Shown are 75 amino acids surrounding the mutation position (marked
with a black box).
Additional file 4: Supplementary Figure 4. Secondary structure
prediction of CFB. To predict protein secondary structure when CFB allele
change, CFSSP: Chou & Fasman Secondary Structure Prediction Server(http://www.biogem.org/tool/chou-fasman/index.php) was used. (A)Secondary structure of C allele (arginine) and (B) T allele (tryptophan) in
CFB rs12614. H, E, T, and C indicate helix structure, sheet structure, turnand coil structure, respectively.
Additional file 5: Supplementary Table 1. Characteristics of study
subjects.
Additional file 6: Supplementary Table 2. Genotype distribution of
investigated CFB genetic polymorphisms among subjects investigated inthis study.
Additional file 7: Supplementary Table 3. Association analysis of
rs12614 using the Training and Test sets.
Additional file 8: Supplementary Table 4. Combined genetic effects
of eleven CHB susceptible loci.
Abbreviations
CHB: Chronic hepatitis B; CFB: Complement factor B; SNP: Single nucleotide
polymorphism; GRSs: Genetic risk scores; HBV: Hepatitis B virus;

Acknowledgements
The authors would like to thank to the patients and their families.

Funding
This research was supported by Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education (Grant No. 2017R1D1A1B03030763), the Ministry of Education,
Science and Technology (NRF-2015R1A2A1A15053987), and grants from the
Korea Health Technology R&D Project through the Korea Health Industry
Development Institute (KHIDI) funded by the Ministry of Health & Welfare,
Republic of Korea (Grant No. HI16C1074) and Yuhan Corporation (Grant No.
800–20170050).
Availability of data and materials
All statistic data generated or analyzed during this study are included in thispublished article and its supplementary information files.
Ethics approval and consent to participate
The study was approved by the institutional review board of Seoul National
University Hospital, Ajou University Medical Center, and Ulsan University
Hospital and performed in accordance with the principles expressed in the
Declaration of Helsinki. All the subjects participating in the study provided
written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Current address: Department of Core Technology, R&D Center, LG
Household & Healthcare (LG H&H), Seoul 07795, South Korea. 2Departmentof Life Science, Sogang University, Seoul 04107, Republic of Korea. 3ResearchInstitute for Basic Science, Sogang University, Seoul 04107, Republic of Korea.
4
Department of Genetic Epidemiology, SNP Genetics Inc., Seoul 04107,
Republic of Korea. 5Department of Internal Medicine and Liver ResearchInstitute, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 03080,
Republic of Korea.
Received: 24 July 2020 Accepted: 23 November 2020

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